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  • 1.
    Baudette, Maxime
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Singh, Ravi Shankar
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Peric, Vedran S.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Løvlund, Stig
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    ’In silico’ testing of a real-time PMU-based tool for power system mode estimation2016In: 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, IEEE Computer Society, 2016, p. 1-5, article id 7741638Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of the software implementation of a real-time mode estimator application and its testing. The application was developed to estimate inter-area modes from both ambient and ring-down synchrophasor data from multiple phasor measurement units (PMU). The software application was implemented in LabVIEW using Statnett’s synchrophasor software development kit (S3DK), to receive real-time synchrophasor measurements. The different features of the application were tested using two types of experiments presented herein. The first experiment is performed using emulated signals from a simple linear model. The second experiment was designed to use a linearized representation of the KTH-Nordic32 power system model. These experiments are used to carry out quantitative analyses of the tool’s performance.

  • 2.
    Peric, Vedran
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Non-intrusive Methods for Mode Estimation in Power Systems using Synchrophasors2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Real-time monitoring of electromechanical oscillations is of great significance for power system operators; to this aim, software solutions (algorithms) that use synchrophasor measurements have been developed for this purpose. This thesis investigates different approaches for improving mode estimation process by offering new methods and deepening the understanding of different stages in the mode estimation process.

    One of the problems tackled in this thesis is the selection of synchrophasor signals used as the input for mode estimation. The proposed selection is performed using a quantitative criterion that is based on the variance of the critical mode estimate. The proposed criterion and associated selection method, offer a systematic and quantitative approach for PMU signal selection. The thesis also analyzes methods for model order selection used in mode estimation. Further, negative effects of forced oscillations and non-white noise load random changes on mode estimation results have been addressed by exploiting the intrinsic power system property that the characteristics of electromechanical modes are predominately determined by the power generation and transmission network.

    An improved accuracy of the mode estimation process can be obtained by intentionally injecting a probing disturbance. The thesis presents an optimization method that finds the optimal spectrum of the probing signals. In addition, the probing signal with the optimal spectrum is generated considering arbitrary time domain signal constraints that can be imposed by various probing signal generating devices.

    Finally, the thesis provides a comprehensive description of a practical implementation of a real-time mode estimation tool. This includes description of the hardware, software architecture, graphical user interface, as well as details of the most important components such as the Statnett’s SDK that allows easy access to synchrophasor data streams.

    Download full text (pdf)
    Thesis
  • 3.
    Peric, Vedran
    et al.
    KTH, School of Electrical Engineering (EES).
    Bombois, Xavier
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Optimal Signal Selection for Power System Ambient Mode Estimation Using a Prediction Error Criterion2016In: 2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), IEEE , 2016Conference paper (Refereed)
  • 4.
    Peric, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Baudette, Maxime
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Gjerde, J. O.
    Løvlund, S.
    Implementation and testing of a real-time mode estimation algorithm using ambient PMU data2014In: 2014 Clemson University Power Systems Conference, PSC 2014, IEEE Computer Society, 2014, p. 6808116-Conference paper (Refereed)
    Abstract [en]

    This paper presents a software implementation of a real-time power system mode estimator application which uses ambient synchrophasor data. The software is built using Statnett's Synchrophasor Software Development Kit (SDK) as a platform for fast prototyping of real-time synchrophasor applications. The SDK extracts synchrophasor data received in the IEEE C.37.118 protocol and provides them as LabVIEW signals. These signals are preprocessed and mode frequencies and damping ratios are calculated by Yule-Walker's method. The implemented LabVIEW software employs state machine logics which enables modifications and upgrades to the algorithm.

  • 5.
    Peric, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Power-System Ambient-Mode Estimation Considering Spectral Load Properties2014In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 29, no 3, p. 1133-1143Article in journal (Refereed)
    Abstract [en]

    Existing mode meter algorithms were derived with the assumption that load variations are accurately represented by white noise or an integral of white noise, which may not be satisfied in actual power systems. This paper proposes a mode meter algorithm which relaxes this assumption by explicitly taking into account spectral load characteristics. These characteristics can be either measured or estimated using the inverse of the existing power system model. The method is developed assuming an autoregressive moving average (ARMA) model of the system and incorporating estimated correlations between loads as inputs and synchrophasor measurements as outputs. Performances of the proposed method are compared with the Yule-Walker and N4SID methods using simulated synchrophasor data obtained from the KTH Nordic 32 test system. Finally, the effects of measurement noise on the proposed method are analyzed, as well as the effects of model uncertainty when the power system model is used to determine spectral load characteristics. It is shown that the proposed algorithm increases accuracy in mode estimates when the loads are described with nonwhite noise.

  • 6.
    Perić, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Bogodorova, Tetiana
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Mete, A. N.
    Vanfretti, Luigi
    Statnett SF, Norway.
    Model order selection for probing-based power system mode estimation2015In: 2015 IEEE Power and Energy Conference at Illinois, PECI 2015, 2015Conference paper (Refereed)
    Abstract [en]

    The paper discusses model order selection for probing mode estimation algorithms. Four methods are analyzed and compared: 1) Residual analysis based model order selection, 2) Model order selection based on singular values, 3) Akaike Information Criterion, and 4) Variance-Accounted-For (VAF) as a measure of optimal fitting between measured data and model response. The methods are assessed using synthetic PMU measurements from the simulation of the KTH Nordic 32 Test system and the IEEE test system with 50 generators and 145 buses.

  • 7.
    Perić, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES).
    Bombois, Xavier
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES).
    Optimal Signal Selection for Power System Ambient Mode Estimation Using a Prediction Error Criterion2016In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 31, no 4, p. 2621-2633Article in journal (Refereed)
    Abstract [en]

    This paper formulates an optimality criterion for the selection of synchrophasor signals to be used in ambient mode estimators. This criterion, which is associated with each measured signal and each dominant mode, is defined as the asymptotic variance of the corresponding estimated mode damping ratio. The value of the criterion is computed directly from an estimated autoregressive moving average (ARMA) model. Because the online computation of the defined criterion (for each measured signal in the system) may be computationally expensive, a fast pre-selection method for initial signal ranking is formulated. The pre-selection method is used to effectively determine a set of the candidate signals for which the formal criterion is evaluated in a second stage. The methodology is illustrated using synthetic measurements from the KTH Nordic 32 and the IEEE 39-bus test systems.

  • 8.
    Perić, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES).
    Sarić, A. T.
    Grabež, D. I.
    Coordinated tuning of power system stabilizers based on Fourier Transform and neural networks2012In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 88, p. 78-88Article in journal (Refereed)
    Abstract [en]

    This paper analyzes optimal tuning of power system stabilizers (PSSs) as the main resource for small-signal stability enhancement in power systems. The procedure is based on dynamic power system response and its frequency amplitude spectrum. Since the optimization model is very complex, there are difficulties in defining the algebraic relation between optimization criteria and PSS parameters and the authors concluded that classical optimization techniques are inappropriate for application in practice. To avoid these problems, application of artificial neural networks (ANNs) as efficient functional approximators is proposed. Optimal PSS parameters are determined by trust region based optimization, where the ANN represents an input function. Robustness of the optimization is ensured with the proposed ANN structure which considers an arbitrary number of different power system operating conditions (including single contingencies). For verification of the proposed methodology, two test systems are used: the New England-New York 68-node, 16-machine test system and the 75-machine dynamic model of the Serbian power system. Poorly damped modes of oscillation are identified and damped by installation of PSSs at appropriate locations with ANN-based optimally tuned parameters.

  • 9.
    Perić, Vedran S.
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Vanfretti, Luigi
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Power System Ambient Mode Estimation Considering Spectral Load Properties2014In: 2014 IEEE PES GENERAL MEETING - CONFERENCE & EXPOSITION, IEEE, 2014Conference paper (Refereed)
  • 10.
    Vanfretti, Luigi
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Aarstrand, Vemund H.
    Statnett SF.
    Almas, Muhammad Shoaib
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Peric, Vedran S.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Gjerde, Jan Ove
    Statnett SF.
    A Software Development Toolkit for Real-Time Synchrophasor Applications2013In: 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013, 2013Conference paper (Refereed)
    Abstract [en]

    This article presents a software development toolkit for Wide Area Monitoring Systems. By using this development toolkit, a researcher is able to manipulate synchrophasor data in the LabView environment, which enables fast software prototyping and testing. This toolkit makes full scale testing in real-time easier for researchers, liberating them of complex and time consuming synchrophasor data handling. The toolkit exploits the IEEE C37.118.2 – 2011 protocol making it independent of any specific equipment and their manufacturers. An application of the development kit is demonstrated in a laboratory environment with a specially designed experimental setup composed of a real-time digital simulator and four phasor measurement units (PMUs).

    Download full text (pdf)
    Almas_Luigi_PowerTech_2013
  • 11.
    Vanfretti, Luigi
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Bengtsson, Sebastian
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Peric, Vedran S.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Gjerde, J. O.
    Effects of forced oscillations in power system damping estimation2012In: 2012 IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2012 Proceedings, IEEE , 2012, p. 59-64Conference paper (Refereed)
    Abstract [en]

    This article analyzes the impact of forced power system oscillations on mode damping estimation. Parametric (Yule-Walker) and non-parametric (Welch) methods for mode estimation are tested in the presence of forced power system oscillations. For mode damping estimation based on non-parametric methods, an application of Half Power Point method is proposed. Performances of the mode estimators are evaluated using both simulated and real synchrophasor data from the Nordic Grid. The presence of forced oscillations poses difficulties to mode damping estimators, these difficulties are identified, illustrated and explained herein.

  • 12.
    Vanfretti, Luigi
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Bengtsson, Sebastian
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Peric, Vedran S.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Gjerde, J. O.
    Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations2013In: 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013, IEEE conference proceedings, 2013, p. 6652190-Conference paper (Refereed)
    Abstract [en]

    Spectral analysis applied to synchrophasor data can provide valuable information about lightly damped low-frequency modes in power systems. This paper demonstrates application of two non-parametric spectral estimators focusing on mode frequency estimation. The first one is the well-known Welch spectral estimator whereas the application of Multitaper method is proposed here. In addition, the paper discusses mode estimator tuning procedures and the estimators' performances in the presence of 'forced' oscillations. The validity of the proposed application of the non-parametric estimators and tuning procedures is verified through both simulated data and PMU data originating from the high-voltage grid of the Nordic power system. Special attention is given to the analysis of the behaviour of different low frequency modes present in the Nordic grid, including that of forced oscillations.

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    fulltext
1 - 12 of 12
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